class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def _doc2index(self, document):
      output = [
          self.word2index[token]
          if token in self.vocab
          else self.unknown_value
          for token in document
      ]
      if self.padding == "max":
          if len(output) < self.max_length:
              output += [self.padding_value] * (
                  self.max_length - len(output)
              )
          else:
              output = output[: self.max_length]
      return output
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 3 for calling the WordVectorTransformer obj's underlying member method code with example usage: obj._doc2index(document) and returns: output